Stratified Randomization for Clinical Trials

Kernan, Walter N.; Viscoli, Catherine M.; Makuch, Robert W.; Brass, Lawrence M.; Horwitz, Ralph I.

doi:10.1016/s0895-4356(98)00138-3

Cited by 523 publications

(417 citation statements)

References 32 publications

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“…Eligibility Criteria: Eligible individuals must: (1) have been assigned a male sex at birth and identify as male at the time of enrollment into the study (cis-gender male); (2) be between the ages of [13][14][15][16][17][18] (inclusive); (3) speak and read English; (4) report same-sex attractions and/or behaviors; (5) have access to the internet, (6) live in one of the zip codes at least partially contained in the 109 counties included in the four regions selected for this trial, and (7) self-report as HIV-negative at time of enrollment.…”

Section: Methods: Trial Designmentioning

confidence: 99%

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Reducing HIV Vulnerability Through a Multilevel Life Skills Intervention for Adolescent Men (The iREACH Project): Protocol for a Randomized Controlled Trial (Preprint)

Bauermeister¹,

Sullivan²,

Gravens³

et al. 2018

Preprint

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Background: Few HIV interventions have demonstrated efficacy in reducing HIV risk among adolescent men who have sex with men (AMSM), and fewer still have recognized the unique needs of AMSM based on race/ethnicity or geographical setting. Recognizing that youths' HIV vulnerability is intricately tied to their development and social context, delivering life skills training during adolescence might delay the onset or reduce the consequences of risk factors for HIV acquisition and equip AMSM with the skills to navigate HIV prevention. This protocol describes the development and testing of iREACH, an online multilevel life skills intervention for AMSM.

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Section: Methods: Trial Designmentioning

confidence: 99%

“…We employ the term AMSM to refer to cis-gender males aged [13][14][15][16][17][18] who may express same-sex attractions and/or engage in same-sex behaviors, yet may or may not identify as gay, bisexual, queer and/or questioning (GBQQ). AMSM also represent a younger age range than YMSM, which typically includes ages up to 24 years.…”

Section: Introductionmentioning

confidence: 99%

Reducing HIV Vulnerability Through a Multilevel Life Skills Intervention for Adolescent Men (The iREACH Project): Protocol for a Randomized Controlled Trial (Preprint)

Bauermeister¹,

Sullivan²,

Gravens³

et al. 2018

Preprint

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“…Successful randomization – even distribution of baseline characteristics between study arms – reduces or eliminates the threat of confounding 4, 5, 6. Stratified randomization is designed to reduce the likelihood of imbalanced trial arms by evenly separating a trial sample between treatment arms based on prespecified prognostic variables.…”

Section: Introductionmentioning

confidence: 99%

“…Stratified randomization is designed to reduce the likelihood of imbalanced trial arms by evenly separating a trial sample between treatment arms based on prespecified prognostic variables. While stratified randomization is an effective means of reducing imbalances within the chosen variables, the methodology is not designed to reduce imbalance among other variables, prognostic or otherwise 4. In ALS, two commonly used baseline characteristics for stratification are riluzole use (RU) and bulbar onset (BO) (e.g., 7, 8, 9).…”

Section: Introductionmentioning

confidence: 99%

Improved stratification of ALS clinical trials using predicted survival

Berry

Taylor

Beaulieu

et al. 2018

Ann Clin Transl Neurol

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IntroductionIn small trials, randomization can fail, leading to differences in patient characteristics across treatment arms, a risk that can be reduced by stratifying using key confounders. In ALS trials, riluzole use (RU) and bulbar onset (BO) have been used for stratification. We hypothesized that randomization could be improved by using a multifactorial prognostic score of predicted survival as a single stratifier.MethodsWe defined a randomization failure as a significant difference between treatment arms on a characteristic. We compared randomization failure rates when stratifying for RU and BO (“traditional stratification”) to failure rates when stratifying for predicted survival using a predictive algorithm. We simulated virtual trials using the PRO‐ACT database without application of a treatment effect to assess balance between cohorts. We performed 100 randomizations using each stratification method – traditional and algorithmic. We applied these stratification schemes to a randomization simulation with a treatment effect using survival as the endpoint and evaluated sample size and power.ResultsStratification by predicted survival met with fewer failures than traditional stratification. Stratifying predicted survival into tertiles performed best. Stratification by predicted survival was validated with an external dataset, the placebo arm from the BENEFIT‐ALS trial. Importantly, we demonstrated a substantial decrease in sample size required to reach statistical power.ConclusionsStratifying randomization based on predicted survival using a machine learning algorithm is more likely to maintain balance between trial arms than traditional stratification methods. The methodology described here can translate to smaller, more efficient clinical trials for numerous neurological diseases.

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“…Control of such variability can lower the occurrence of type-l errors and improve power for small trials. While it is important to define stratification factors that have a relevant effect on prognosis or treatment responsiveness, it is also important to avoid creating too many strata 1 . Most literature on stratification strategies is based on interventions at an individual (e.g., patient) level.…”

Section: Introductionmentioning

confidence: 99%

Pre-randomization decisions and group stratification in a randomized controlled trial to improve prescribing

et al. 2004

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Pre-randomization decisions and group stratification in a randomized controlled trial to improve prescribing • M a r t i n e E . C . v a n E i j k , A r s e n i o H . P. P a e s , A r i j a n P o r s i u s , J e r r y A v o r n a n d A n t h o n i u s d e B o e r Main outcome measures:Identification and evaluation of primary care practice group characteristics related to changes in prescribing. These characteristics were evaluated by estimation of incidence rate ratios (Poisson regression).Results: Of practice groups 40 representatives (66%) participated in our study. Three characteristics were found to be most relevant for stratification: the purpose of the practice groups (whether they participated in information exchange (n ϭ 14) or binding consensus on pharmacotherapy (n ϭ 26)), the use of a formulary (22 practice groups did and 18 groups did not) and the use of feedback data (22 groups did and 18 groups did not). These characteristics strongly modified the effect of the program on prescribing behaviour. While the overall effect of the program was to significantly reduce the prescribing of the targeted drugs, this change was not consistent in all strata. Conclusion: Assessment of the characteristics of practice groups made it possible to define factors to be used for a stratified randomization of practice groups which in retrospect indeed modified the effect of an educational intervention directed to change prescribing. Such pre-randomization assessments can be a useful tool in interventions designed to improve practice patterns in groups of physicians.

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Stratified Randomization for Clinical Trials

Cited by 523 publications

References 32 publications

Reducing HIV Vulnerability Through a Multilevel Life Skills Intervention for Adolescent Men (The iREACH Project): Protocol for a Randomized Controlled Trial (Preprint)

Reducing HIV Vulnerability Through a Multilevel Life Skills Intervention for Adolescent Men (The iREACH Project): Protocol for a Randomized Controlled Trial (Preprint)

Improved stratification of ALS clinical trials using predicted survival

Pre-randomization decisions and group stratification in a randomized controlled trial to improve prescribing

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